This document provides an overview of data mining concepts and techniques. It discusses topics such as predictive analytics, machine learning, pattern recognition, and artificial intelligence as they relate to data mining. It also covers specific data mining algorithms like decision trees, neural networks, and association rules. The document discusses supervised and unsupervised learning approaches and explains model evaluation techniques like accuracy, ROC curves, gains/lift curves, and cross-entropy. It emphasizes the importance of evaluating models on test data and monitoring performance over time as patterns change.
Classification is a data analysis technique used to predict class membership for new observations based on a training set of previously labeled examples. It involves building a classification model during a training phase using an algorithm, then testing the model on new data to estimate accuracy. Some common classification algorithms include decision trees, Bayesian networks, neural networks, and support vector machines. Classification has applications in domains like medicine, retail, and entertainment.
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
This document discusses decision trees and entropy. It begins by providing examples of binary and numeric decision trees used for classification. It then describes characteristics of decision trees such as nodes, edges, and paths. Decision trees are used for classification by organizing attributes, values, and outcomes. The document explains how to build decision trees using a top-down approach and discusses splitting nodes based on attribute type. It introduces the concept of entropy from information theory and how it can measure the uncertainty in data for classification. Entropy is the minimum number of questions needed to identify an unknown value.
Survey on Various Classification Techniques in Data Miningijsrd.com
Dynamic Classification is an information mining (machine learning) strategy used to anticipate bunch participation for information cases. In this paper, we show the essential arrangement systems. A few significant sorts of arrangement technique including induction, Bayesian networks, k-nearest neighbor classifier, case-based reasoning, genetic algorithm and fuzzy logic techniques. The objective of this review is to give a complete audit of distinctive characterization procedures in information mining.
Classification and prediction models are used to categorize data or predict unknown values. Classification predicts categorical class labels to classify new data based on attributes in a training set, while prediction models continuous values. Common applications include credit approval, marketing, medical diagnosis, and treatment analysis. The classification process involves building a model from a training set and then using the model to classify new data, estimating accuracy on a test set.
Classification techniques in data miningKamal Acharya
The document discusses classification algorithms in machine learning. It provides an overview of various classification algorithms including decision tree classifiers, rule-based classifiers, nearest neighbor classifiers, Bayesian classifiers, and artificial neural network classifiers. It then describes the supervised learning process for classification, which involves using a training set to construct a classification model and then applying the model to a test set to classify new data. Finally, it provides a detailed example of how a decision tree classifier is constructed from a training dataset and how it can be used to classify data in the test set.
Classification is a popular data mining technique that assigns items to target categories or classes. It builds models called classifiers to predict the class of records with unknown class labels. Some common applications of classification include fraud detection, target marketing, and medical diagnosis. Classification involves a learning step where a model is constructed by analyzing a training set with class labels, and a classification step where the model predicts labels for new data. Supervised learning uses labeled data to train machine learning algorithms to produce correct outcomes for new examples.
Classification is a data analysis technique used to predict class membership for new observations based on a training set of previously labeled examples. It involves building a classification model during a training phase using an algorithm, then testing the model on new data to estimate accuracy. Some common classification algorithms include decision trees, Bayesian networks, neural networks, and support vector machines. Classification has applications in domains like medicine, retail, and entertainment.
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
This document discusses decision trees and entropy. It begins by providing examples of binary and numeric decision trees used for classification. It then describes characteristics of decision trees such as nodes, edges, and paths. Decision trees are used for classification by organizing attributes, values, and outcomes. The document explains how to build decision trees using a top-down approach and discusses splitting nodes based on attribute type. It introduces the concept of entropy from information theory and how it can measure the uncertainty in data for classification. Entropy is the minimum number of questions needed to identify an unknown value.
Survey on Various Classification Techniques in Data Miningijsrd.com
Dynamic Classification is an information mining (machine learning) strategy used to anticipate bunch participation for information cases. In this paper, we show the essential arrangement systems. A few significant sorts of arrangement technique including induction, Bayesian networks, k-nearest neighbor classifier, case-based reasoning, genetic algorithm and fuzzy logic techniques. The objective of this review is to give a complete audit of distinctive characterization procedures in information mining.
Classification and prediction models are used to categorize data or predict unknown values. Classification predicts categorical class labels to classify new data based on attributes in a training set, while prediction models continuous values. Common applications include credit approval, marketing, medical diagnosis, and treatment analysis. The classification process involves building a model from a training set and then using the model to classify new data, estimating accuracy on a test set.
Classification techniques in data miningKamal Acharya
The document discusses classification algorithms in machine learning. It provides an overview of various classification algorithms including decision tree classifiers, rule-based classifiers, nearest neighbor classifiers, Bayesian classifiers, and artificial neural network classifiers. It then describes the supervised learning process for classification, which involves using a training set to construct a classification model and then applying the model to a test set to classify new data. Finally, it provides a detailed example of how a decision tree classifier is constructed from a training dataset and how it can be used to classify data in the test set.
Classification is a popular data mining technique that assigns items to target categories or classes. It builds models called classifiers to predict the class of records with unknown class labels. Some common applications of classification include fraud detection, target marketing, and medical diagnosis. Classification involves a learning step where a model is constructed by analyzing a training set with class labels, and a classification step where the model predicts labels for new data. Supervised learning uses labeled data to train machine learning algorithms to produce correct outcomes for new examples.
Exploratory Data Analysis (EDA) was promoted by John Tukey in 1977 to encourage visually examining data without hypotheses. EDA uses graphical and non-graphical techniques like histograms, scatter plots, box plots to summarize variable characteristics. EDA allows understanding data distributions and relationships without models through inspection and information graphics. Common EDA goals are describing typical values, variability, distributions, and relationships between variables.
The document discusses the differences and similarities between classification and prediction, providing examples of how classification predicts categorical class labels by constructing a model based on training data, while prediction models continuous values to predict unknown values, though the process is similar between the two. It also covers clustering analysis, explaining that it is an unsupervised technique that groups similar data objects into clusters to discover hidden patterns in datasets.
This document discusses clustering, which is the task of grouping data points into clusters so that points within the same cluster are more similar to each other than points in other clusters. It describes different types of clustering methods, including density-based, hierarchical, partitioning, and grid-based methods. It provides examples of specific clustering algorithms like K-means, DBSCAN, and discusses applications of clustering in fields like marketing, biology, libraries, insurance, city planning, and earthquake studies.
2.1 Data Mining-classification Basic conceptsKrish_ver2
This document discusses classification and decision trees. It defines classification as predicting categorical class labels using a model constructed from a training set. Decision trees are a popular classification method that operate in a top-down recursive manner, splitting the data into purer subsets based on attribute values. The algorithm selects the optimal splitting attribute using an evaluation metric like information gain at each step until it reaches a leaf node containing only one class.
Chapter - 6 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
The document describes Chapter 6 of the book "Data Mining: Concepts and Techniques" which covers the topics of classification and prediction. It defines classification and prediction and discusses key issues in classification such as data preparation, evaluating methods, and decision tree induction. Decision tree induction creates a tree model by recursively splitting the training data on attributes and their values to make predictions. The chapter also covers other classification methods like Bayesian classification, rule-based classification, and support vector machines. It describes the process of model construction from training data and then using the model to classify new, unlabeled data.
Data Analysis: Evaluation Metrics for Supervised Learning Models of Machine L...Md. Main Uddin Rony
This document discusses various machine learning evaluation metrics for supervised learning models. It covers classification, regression, and ranking metrics. For classification, it describes accuracy, confusion matrix, log-loss, and AUC. For regression, it discusses RMSE and quantiles of errors. For ranking, it explains precision-recall, precision-recall curves, F1 score, and NDCG. The document provides examples and visualizations to illustrate how these metrics are calculated and used to evaluate model performance.
This document provides an overview of data mining techniques discussed in Chapter 3, including parametric and nonparametric models, statistical perspectives on point estimation and error measurement, Bayes' theorem, decision trees, neural networks, genetic algorithms, and similarity measures. Nonparametric techniques like neural networks, decision trees, and genetic algorithms are particularly suitable for data mining applications involving large, dynamically changing datasets.
Knowledge Discovery Tutorial By Claudia d'Amato and Laura Hollnik at the Summer School on Ontology Engineering and the Semantic Web in Bertinoro, Italy (SSSW2015)
Valencian Summer School 2015
Day 2
Lecture 11
The Future of Machine Learning
José David Martín-Guerrero (IDAL, UV)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
This document outlines the learning objectives and resources for a course on data mining and analytics. The course aims to:
1) Familiarize students with key concepts in data mining like association rule mining and classification algorithms.
2) Teach students to apply techniques like association rule mining, classification, cluster analysis, and outlier analysis.
3) Help students understand the importance of applying data mining concepts across different domains.
The primary textbook listed is "Data Mining: Concepts and Techniques" by Jiawei Han and Micheline Kamber. Topics that will be covered include introduction to data mining, preprocessing, association rules, classification algorithms, cluster analysis, and applications.
This document summarizes a presentation about machine learning and predictive analytics. It discusses formal definitions of machine learning, the differences between supervised and unsupervised learning, examples of machine learning applications, and evaluation metrics for predictive models like lift, sensitivity, and accuracy. Key machine learning algorithms mentioned include logistic regression and different types of modeling. The presentation provides an overview of concepts in machine learning and predictive analytics.
This document defines key concepts in data mining tasks and knowledge representation. It discusses (1) task relevant data, background knowledge, interestingness measures, input/output representation, and visualization techniques used in data mining; (2) examples of concept hierarchies like schema, set-grouping, and rule-based hierarchies; and (3) common visualization techniques like histograms, scatterplots, and box plots used to analyze and present data mining results.
A General Framework for Accurate and Fast Regression by Data Summarization in...Yao Wu
1. The document proposes a framework called Random Decision Trees (RDT) for fast and accurate regression, classification, and probability estimation using data summarization.
2. RDT builds multiple randomized decision trees on training data where the structure of each tree is randomly generated and node statistics are summarized.
3. To make predictions, the predictions from each randomized tree are averaged, which improves accuracy and reduces overfitting compared to other models like decision trees, boosting, and bagging.
The document discusses frequent pattern mining and the Apriori algorithm. It can be summarized as follows:
1) Frequent pattern mining is used to find patterns that frequently occur together in a transaction database. The Apriori algorithm is an influential algorithm for mining frequent itemsets using an iterative, candidate generation and test approach.
2) The Apriori algorithm generates candidate itemsets of length k from frequent itemsets of length k-1, and then prunes the candidates that have a subset that is infrequent. This is repeated until no further frequent itemsets are found.
3) Once frequent itemsets are discovered, association rules can be generated from them if they satisfy minimum support and confidence thresholds.
This document discusses classification and prediction. Classification predicts categorical class labels by classifying data based on a training set and class labels. Prediction models continuous values and predicts unknown values. Some applications are credit approval, marketing, medical diagnosis, and treatment analysis. Classification involves a learning step to describe classes and a classification step to classify new data. Prediction involves estimating accuracy by comparing test results to known labels. Issues with classification and prediction include data preparation, comparing methods, and decision tree induction algorithms.
This document provides an overview of decision trees, including:
- Decision trees can classify data quickly, achieve accuracy similar to other models, and are simple to understand.
- A decision tree has root, internal, and leaf nodes organized in a top-down structure to partition data based on attribute tests.
- To classify a record, the attribute tests are applied from the root node down until a leaf node is reached, which assigns the record's class.
- Decision trees require attribute-value data, predefined target classes, and sufficient training data to learn the model.
Why do People Share Online - Study by the NYTimesRick Ramos
This study examined the motivations behind why consumers share content online. Researchers conducted ethnographies, in-person interviews, and an online survey of 2,500 regular sharers. They found that sharing fulfills psychological and social needs such as connecting with others, self-expression, feeling involved in the world, and supporting causes. Six distinct sharing personas emerged based on their motivations, values, and role of sharing in life. The study concluded that to get content widely shared, marketers should appeal to consumers' motivations of connecting with others rather than just promoting brands, build trust, keep messages simple, and appeal to humor.
How I got 2.5 Million views on Slideshare (by @nickdemey - Board of Innovation)Board of Innovation
This document provides tips for creating engaging slide decks on SlideShare that garner many views. It recommends focusing on quality over quantity when creating each slide, using compelling images and headlines, and including calls to action throughout. It also suggests experimenting with sharing techniques and doing so in waves to build momentum. The goal is to create decks that are optimized for sharing and spread across multiple channels over time.
Exploratory Data Analysis (EDA) was promoted by John Tukey in 1977 to encourage visually examining data without hypotheses. EDA uses graphical and non-graphical techniques like histograms, scatter plots, box plots to summarize variable characteristics. EDA allows understanding data distributions and relationships without models through inspection and information graphics. Common EDA goals are describing typical values, variability, distributions, and relationships between variables.
The document discusses the differences and similarities between classification and prediction, providing examples of how classification predicts categorical class labels by constructing a model based on training data, while prediction models continuous values to predict unknown values, though the process is similar between the two. It also covers clustering analysis, explaining that it is an unsupervised technique that groups similar data objects into clusters to discover hidden patterns in datasets.
This document discusses clustering, which is the task of grouping data points into clusters so that points within the same cluster are more similar to each other than points in other clusters. It describes different types of clustering methods, including density-based, hierarchical, partitioning, and grid-based methods. It provides examples of specific clustering algorithms like K-means, DBSCAN, and discusses applications of clustering in fields like marketing, biology, libraries, insurance, city planning, and earthquake studies.
2.1 Data Mining-classification Basic conceptsKrish_ver2
This document discusses classification and decision trees. It defines classification as predicting categorical class labels using a model constructed from a training set. Decision trees are a popular classification method that operate in a top-down recursive manner, splitting the data into purer subsets based on attribute values. The algorithm selects the optimal splitting attribute using an evaluation metric like information gain at each step until it reaches a leaf node containing only one class.
Chapter - 6 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
The document describes Chapter 6 of the book "Data Mining: Concepts and Techniques" which covers the topics of classification and prediction. It defines classification and prediction and discusses key issues in classification such as data preparation, evaluating methods, and decision tree induction. Decision tree induction creates a tree model by recursively splitting the training data on attributes and their values to make predictions. The chapter also covers other classification methods like Bayesian classification, rule-based classification, and support vector machines. It describes the process of model construction from training data and then using the model to classify new, unlabeled data.
Data Analysis: Evaluation Metrics for Supervised Learning Models of Machine L...Md. Main Uddin Rony
This document discusses various machine learning evaluation metrics for supervised learning models. It covers classification, regression, and ranking metrics. For classification, it describes accuracy, confusion matrix, log-loss, and AUC. For regression, it discusses RMSE and quantiles of errors. For ranking, it explains precision-recall, precision-recall curves, F1 score, and NDCG. The document provides examples and visualizations to illustrate how these metrics are calculated and used to evaluate model performance.
This document provides an overview of data mining techniques discussed in Chapter 3, including parametric and nonparametric models, statistical perspectives on point estimation and error measurement, Bayes' theorem, decision trees, neural networks, genetic algorithms, and similarity measures. Nonparametric techniques like neural networks, decision trees, and genetic algorithms are particularly suitable for data mining applications involving large, dynamically changing datasets.
Knowledge Discovery Tutorial By Claudia d'Amato and Laura Hollnik at the Summer School on Ontology Engineering and the Semantic Web in Bertinoro, Italy (SSSW2015)
Valencian Summer School 2015
Day 2
Lecture 11
The Future of Machine Learning
José David Martín-Guerrero (IDAL, UV)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
This document outlines the learning objectives and resources for a course on data mining and analytics. The course aims to:
1) Familiarize students with key concepts in data mining like association rule mining and classification algorithms.
2) Teach students to apply techniques like association rule mining, classification, cluster analysis, and outlier analysis.
3) Help students understand the importance of applying data mining concepts across different domains.
The primary textbook listed is "Data Mining: Concepts and Techniques" by Jiawei Han and Micheline Kamber. Topics that will be covered include introduction to data mining, preprocessing, association rules, classification algorithms, cluster analysis, and applications.
This document summarizes a presentation about machine learning and predictive analytics. It discusses formal definitions of machine learning, the differences between supervised and unsupervised learning, examples of machine learning applications, and evaluation metrics for predictive models like lift, sensitivity, and accuracy. Key machine learning algorithms mentioned include logistic regression and different types of modeling. The presentation provides an overview of concepts in machine learning and predictive analytics.
This document defines key concepts in data mining tasks and knowledge representation. It discusses (1) task relevant data, background knowledge, interestingness measures, input/output representation, and visualization techniques used in data mining; (2) examples of concept hierarchies like schema, set-grouping, and rule-based hierarchies; and (3) common visualization techniques like histograms, scatterplots, and box plots used to analyze and present data mining results.
A General Framework for Accurate and Fast Regression by Data Summarization in...Yao Wu
1. The document proposes a framework called Random Decision Trees (RDT) for fast and accurate regression, classification, and probability estimation using data summarization.
2. RDT builds multiple randomized decision trees on training data where the structure of each tree is randomly generated and node statistics are summarized.
3. To make predictions, the predictions from each randomized tree are averaged, which improves accuracy and reduces overfitting compared to other models like decision trees, boosting, and bagging.
The document discusses frequent pattern mining and the Apriori algorithm. It can be summarized as follows:
1) Frequent pattern mining is used to find patterns that frequently occur together in a transaction database. The Apriori algorithm is an influential algorithm for mining frequent itemsets using an iterative, candidate generation and test approach.
2) The Apriori algorithm generates candidate itemsets of length k from frequent itemsets of length k-1, and then prunes the candidates that have a subset that is infrequent. This is repeated until no further frequent itemsets are found.
3) Once frequent itemsets are discovered, association rules can be generated from them if they satisfy minimum support and confidence thresholds.
This document discusses classification and prediction. Classification predicts categorical class labels by classifying data based on a training set and class labels. Prediction models continuous values and predicts unknown values. Some applications are credit approval, marketing, medical diagnosis, and treatment analysis. Classification involves a learning step to describe classes and a classification step to classify new data. Prediction involves estimating accuracy by comparing test results to known labels. Issues with classification and prediction include data preparation, comparing methods, and decision tree induction algorithms.
This document provides an overview of decision trees, including:
- Decision trees can classify data quickly, achieve accuracy similar to other models, and are simple to understand.
- A decision tree has root, internal, and leaf nodes organized in a top-down structure to partition data based on attribute tests.
- To classify a record, the attribute tests are applied from the root node down until a leaf node is reached, which assigns the record's class.
- Decision trees require attribute-value data, predefined target classes, and sufficient training data to learn the model.
Why do People Share Online - Study by the NYTimesRick Ramos
This study examined the motivations behind why consumers share content online. Researchers conducted ethnographies, in-person interviews, and an online survey of 2,500 regular sharers. They found that sharing fulfills psychological and social needs such as connecting with others, self-expression, feeling involved in the world, and supporting causes. Six distinct sharing personas emerged based on their motivations, values, and role of sharing in life. The study concluded that to get content widely shared, marketers should appeal to consumers' motivations of connecting with others rather than just promoting brands, build trust, keep messages simple, and appeal to humor.
How I got 2.5 Million views on Slideshare (by @nickdemey - Board of Innovation)Board of Innovation
This document provides tips for creating engaging slide decks on SlideShare that garner many views. It recommends focusing on quality over quantity when creating each slide, using compelling images and headlines, and including calls to action throughout. It also suggests experimenting with sharing techniques and doing so in waves to build momentum. The goal is to create decks that are optimized for sharing and spread across multiple channels over time.
An impactful approach to the Seven Deadly Sins you and your Brand should avoid on Social Media! From a humoristic approach to a modern-life analogy for Social Media and including everything in between, this deck is a compelling resource that will provide you with more than a few take-aways for your Brand!
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms for those who already suffer from conditions like depression and anxiety.
How People Really Hold and Touch (their Phones)Steven Hoober
The document discusses design guidelines for touchscreen interfaces based on research into how people actually hold and interact with mobile devices. It provides data on finger sizes, common grips, touch targets, and notes that touch interaction is not just about finger size and pinpoint accuracy. The guidelines include making targets visible and tappable, designing for different screen sizes, leaving space for scrolling, and testing interfaces at scale.
You are dumb at the internet. You don't know what will go viral. We don't either. But we are slighter less dumber. So here's a bunch of stuff we learned that will help you be less dumb too.
What 33 Successful Entrepreneurs Learned From FailureReferralCandy
Entrepreneurs encounter failure often. Successful entrepreneurs overcome failure and emerge wiser. We've taken 33 lessons about failure from Brian Honigman's article "33 Entrepreneurs Share Their Biggest Lessons Learned from Failure", illustrated them with statistics and a little story about entrepreneurship... in space!
Rand Fishkin discusses why content marketing often fails and provides 5 key reasons: 1) Unrealistic expectations of how content marketing works, 2) Creating content without a community to amplify it, 3) Focusing on content creation but not amplification, 4) Ignoring search engine optimization, and 5) Giving up too soon and not allowing time for content to gain traction. He emphasizes that content marketing is a long-term process of building relationships and that most successful content took years of iteration before gaining significant reach.
SEO has changed a lot over the last two decades. We all know about Google Panda & Penguin, but did you know there was a time when search engine results were returned by humans? Crazy right? We take a trip down memory lane to chart some of the biggest events in SEO that have helped shape the industry today.
Inside this guide, you'll learn an insiders tips and techniques to getting into the marketing industry - no job applications necessary.
You'll learn what marketing really is, why you'll find a job easily, what entry level marketing jobs look like and four actionable things you can try right now to help get you into the marketing industry.
Visit Inbound.org and the Inbound.org/jobs community jobs board to find opportunities and connect with professional marketers from all over.
The What If Technique presented by Motivate DesignMotivate Design
Why "What If"...?
The What If Technique tackles the challenge of engaging a creative, disruptive mindset when it comes to design thinking and crafting innovative user experiences.
Thinking disruptively is a disruptive thing to do, which means it's a very hard thing to do, especially when you add in risk-averse business leaders and company cultures, who hold on tight to psychological blocks, corporate lore, and excuse personas that stifle creativity and possibilities (see www.motivatedesign.com/what-if for more details).
The What If Technique offers key steps, tools and examples to help you achieve incremental changes that promote disruptive thinking, overcome barriers to creativity, and lead to big, innovative differences for business leaders, companies, and ultimately user experiences and products.
Let's find out what's what together! Explore your "What Ifs" with us. See www.motivatedesign.com/what-if for details about the What If Technique, studio workshops, the book, case studies and more downloads--including a the sample chapter "Corporate Lore and Blocks to Creativity"
Connect with us @Motivate_Design
The document provides principles for presenting data in the clearest way possible: tell the truth and ensure credibility with data; get to the main point by drawing meaning from the data; pick the right tool like pie, bar, or line graphs depending on the data; highlight what's important by keeping slides focused on conclusions, not all data; and keep visuals simple to avoid distractions.
What Would Steve Do? 10 Lessons from the World's Most Captivating PresentersHubSpot
The document provides 10 tips for creating captivating presentations based on lessons from famous presenters like Steve Jobs, Scott Harrison, and Gary Vaynerchuk. The tips include crafting an emotional story with a beginning, middle, and end; creating slides that answer why the audience should care, how it will improve their lives, and what they must do; using simple language without jargon; using metaphors; ditching bullet points; showing rather than just telling through images; rehearsing extensively; and that excellence requires hard work with no shortcuts.
This document provides an overview and introduction to digital strategy from Bud Caddell, SVP and Director of Digital Strategy at Deutsch LA. It defines key terms like digital strategy, digital strategist, and core concepts. It explores what a digital strategy and strategist are, essential concepts like insights, cultural tensions and category conventions, and what deliverables a digital strategist produces. The document is intended to educate young practitioners entering the field of digital strategy.
Today we all live and work in the Internet Century, where technology is roiling the business landscape, and the pace of change is only accelerating.
In their new book How Google Works, Google Executive Chairman and ex-CEO Eric Schmidt and former SVP of Products Jonathan Rosenberg share the lessons they learned over the course of a decade running Google.
Covering topics including corporate culture, strategy, talent, decision-making, communication, innovation, and dealing with disruption, the authors illustrate management maxims with numerous insider anecdotes from Google’s history.
In an era when everything is speeding up, the best way for businesses to succeed is to attract smart-creative people and give them an environment where they can thrive at scale. How Google Works is a new book that explains how to do just that.
This is a visual preview of How Google Works. You can pick up a copy of the book at www.howgoogleworks.net
This document provides an overview of machine learning algorithms and their applications in the financial industry. It begins with brief introductions of the authors and their backgrounds in applying artificial intelligence to retail. It then covers key machine learning concepts like supervised and unsupervised learning as well as algorithms like logistic regression, decision trees, boosting and time series analysis. Examples are provided for how these techniques can be used for applications like predicting loan risk and intelligent loan applications. Overall, the document aims to give a high-level view of machine learning in finance through discussing algorithms and their uses in areas like risk analysis.
This document provides an introduction to machine learning, including definitions, types of machine learning problems, common algorithms, and typical machine learning processes. It defines machine learning as a type of artificial intelligence that enables computers to learn without being explicitly programmed. The three main types of machine learning problems are supervised learning (classification and regression), unsupervised learning (clustering and association), and reinforcement learning. Common machine learning algorithms and examples of their applications are also discussed. The document concludes with an overview of typical machine learning processes such as selecting and preparing data, developing and evaluating models, and interpreting results.
The document discusses MARS (Multivariate Adaptive Regression Splines), a new tool for regression analysis. MARS can automatically select variables, detect interactions between variables, and produce models that are protected against overfitting. It was developed by Jerome Friedman and produces smooth curves rather than step functions like CART. The document provides an introduction to MARS concepts and guidelines for using MARS in practice.
Identifying and classifying unknown Network Disruptionjagan477830
This document discusses identifying and classifying unknown network disruptions using machine learning algorithms. It begins by introducing the problem and importance of identifying network disruptions. Then it discusses related work on classifying network protocols. The document outlines the dataset and problem statement of predicting fault severity. It describes the machine learning workflow and various algorithms like random forest, decision tree and gradient boosting that are evaluated on the dataset. Finally, it concludes with achieving the objective of classifying disruptions and discusses future work like optimizing features and using neural networks.
Pharmacokinetic-pharmacodynamic modeling involves creating mathematical models to represent biological systems. These models use experimentally derived data and can be classified as either models of data or models of systems. Models of data require few assumptions, while models of systems are based on physical principles. The model development process involves analyzing the problem, collecting data, formulating the model, fitting the model to data, validating the model, and communicating results. Model validation assesses how well a model serves its intended purpose, though models can never be fully proven and are disproven through validity testing.
Choosing a Machine Learning technique to solve your needGibDevs
This document discusses choosing a machine learning technique to solve a problem. It begins with an overview of machine learning and popular approaches like linear regression, logistic regression, decision trees, k-means clustering, principal component analysis, support vector machines, and neural networks. It then discusses important considerations like knowing your data, cleaning your data, categorizing the problem, understanding constraints, choosing an algorithm, and evaluating models. Programming languages like Python and libraries, datasets, and cloud support resources are also mentioned.
This document discusses WEKA, an open-source data mining and machine learning tool. It summarizes how WEKA was used to analyze a bike sharing dataset from Washington D.C. to predict bike usage. Different WEKA techniques were explored, including classification algorithms like J48 and Naive Bayes. J48 performed best by visualizing decision trees. Clustering was also attempted but seasonal patterns were only partially distinguished. Overall, the dataset seemed better suited to classification than clustering for predicting bike usage.
Chapter 4 Classification in data sience .pdfAschalewAyele2
This document discusses data mining tasks related to predictive modeling and classification. It defines predictive modeling as using historical data to predict unknown future values, with a focus on accuracy. Classification is described as predicting categorical class labels based on a training set. Several classification algorithms are mentioned, including K-nearest neighbors, decision trees, neural networks, Bayesian networks, and support vector machines. The document also discusses evaluating classification performance using metrics like accuracy, precision, recall, and a confusion matrix.
In this presentation I review various data science techniques and discuss their usefulness to pricing actuaries working in general insurance.
This presentation was originally given at the TIGI webinar in 2020.
https://www.actuaries.org.uk/learn-develop/attend-event/tigi-2020-technical-issues-general-insurance
AI-900 - Fundamental Principles of ML.pptxkprasad8
Automated machine learning uses algorithms to automate the machine learning workflow including data preprocessing, model selection, hyperparameter tuning, and evaluation to build an optimal machine learning model with little or no human involvement. It can save time by automating repetitive tasks and help identify the best performing models for various types of machine learning problems like classification, regression, and clustering. Automated machine learning tools provide an end-to-end experience to build, deploy, and manage machine learning models at scale with minimal coding or machine learning expertise required.
Data mining involves finding hidden patterns in large datasets. It differs from traditional data access in that the query may be unclear, the data has been preprocessed, and the output is an analysis rather than a data subset. Data mining algorithms attempt to fit models to the data by examining attributes, criteria for preference of one model over others, and search techniques. Common data mining tasks include classification, regression, clustering, association rule learning, and prediction.
Machine learning can be used to predict whether a user will purchase a book on an online book store. Features about the user, book, and user-book interactions can be generated and used in a machine learning model. A multi-stage modeling approach could first predict if a user will view a book, and then predict if they will purchase it, with the predicted view probability as an additional feature. Decision trees, logistic regression, or other classification algorithms could be used to build models at each stage. This approach aims to leverage user data to provide personalized book recommendations.
This document discusses various techniques for machine learning when labeled training data is limited, including semi-supervised learning approaches that make use of unlabeled data. It describes assumptions like the clustering assumption, low density assumption, and manifold assumption that allow algorithms to learn from unlabeled data. Specific techniques covered include clustering algorithms, mixture models, self-training, and semi-supervised support vector machines.
This document provides an introduction to random forests, which are an ensemble machine learning method for classification and regression. Random forests build on decision trees but average multiple tree predictions to improve accuracy over a single tree. Each tree is constructed using a random sample of data and random subsets of features. This introduces variability that improves predictive performance compared to single trees or bagged trees that use all features. The document outlines the key characteristics and advantages of random forests, such as high accuracy, ability to handle large datasets with many variables, and resistance to overfitting.
This document discusses various multivariate analysis techniques. It provides an overview of multidimensional scaling (MDS) which maps distances between observations in a high dimensional space to a lower dimensional space. It also discusses data envelopment analysis (DEA) which uses linear programming to evaluate the efficiency of decision making units relative to a efficient frontier. Finally, it notes some conditions and considerations for implementing DEA, such as having homogenous decision making units and a sufficient sample size.
Diabetes Prediction Using Machine Learningjagan477830
Our proposed system aims at Predicting the number of Diabetes patients and eliminating the risk of False Negatives Drastically.
In proposed System, we use Random forest, Decision tree, Logistic Regression and Gradient Boosting Classifier to classify the Patients who are affected with Diabetes or not.
Random Forest and Decision Tree are the algorithms which can be used for both classification and regression.
The dataset is classified into trained and test dataset where the data can be trained individually, these algorithms are very easy to implement as well as very efficient in producing better results and can able to process large amount of data.
Even for large dataset these algorithms are extremely fast and can able to give accuracy of about over 90%.
This document discusses using machine learning techniques to detect vehicle insurance fraud. It introduces machine learning and explores how it can be applied to insurance fraud detection by analyzing past claims data. The document then outlines the steps taken, including exploratory data analysis of the claims dataset, building models using support vector machines, decision trees, and logistic regression, evaluating and selecting the most accurate model, and deploying the selected model for real-world fraud prediction.
Improve Your Regression with CART and RandomForestsSalford Systems
Why You Should Watch: Learn the fundamentals of tree-based machine learning algorithms and how to easily fine tune and improve your Random Forest regression models.
Abstract: In this webinar we'll introduce you to two tree-based machine learning algorithms, CART® decision trees and RandomForests®. We will discuss the advantages of tree based techniques including their ability to automatically handle variable selection, variable interactions, nonlinear relationships, outliers, and missing values. We'll explore the CART algorithm, bootstrap sampling, and the Random Forest algorithm (all with animations) and compare their predictive performance using a real world dataset.
Improved Predictions in Structure Based Drug Design Using Cart and Bayesian M...Salford Systems
The document discusses using in silico methods like virtual screening and predictive modeling to improve drug discovery. It presents results from applying techniques like receptor docking, machine learning algorithms, and Bayesian modeling to develop improved scoring functions that better distinguish active from inactive compounds. These scoring functions helped identify key molecular properties that correlated with active hits. The methods showed improved ability to find active hits compared to previous scoring functions.
Churn Modeling-For-Mobile-Telecommunications Salford Systems
This document summarizes a study on predicting customer churn for a major mobile provider. TreeNet models were used to predict the probability of customers churning (switching providers) within a 30-60 day period. TreeNet models significantly outperformed other methods, increasing accuracy and the proportion of high-risk customers identified. Applying the most accurate TreeNet models could translate to millions in additional annual revenue by helping the provider preemptively retain more customers.
This document provides dos and don'ts for data mining based on experiences from various practitioners. It lists important steps like clearly defining objectives, simplifying solutions, preparing data, using multiple techniques, and checking models. It warns against underestimating preparation, overfitting models, and collecting excessive unhelpful data. Practitioners emphasize the importance of domain knowledge, transparency, and creating models that are understandable to stakeholders.
9 Data Mining Challenges From Data Scientists Like YouSalford Systems
The document outlines 9 challenges faced by data scientists: 1) poor quality data issues like dirty, missing, or inadequate data, 2) lack of understanding of data mining techniques, 3) lack of good literature on important topics and techniques, 4) difficulty for academic institutions accessing commercial-grade software at reasonable costs, 5) accommodating data from different sources and formats, 6) updating models constantly with new incoming data for online machine learning, 7) dealing with huge datasets requiring distributed approaches, 8) determining the right questions to ask of the data, and 9) remaining objective and letting the data lead rather than preconceptions.
This document contains a collection of quotes related to statistics and data. Some key quotes emphasize that while data and information are important, they must be used carefully and combined with human intelligence, judgement, and insight. Other quotes note that statistics can be flexible and misleading if not interpreted carefully, and that collecting quality data over long periods of time is important for analysis. The overall message is that statistics are a useful tool but have limitations, and human discernment is still needed.
Using CART For Beginners with A Teclo Example DatasetSalford Systems
Familiarize yourself with CART Decision Tree technology in this beginner's tutorial using a telecommunications example dataset from the 1990s. By the end of this tutorial you should feel comfortable using CART on your own with sample or real-world data.
The document provides an overview of a 4-part webinar covering the evolution of regression techniques from classical least squares to more advanced machine learning methods like random forests and gradient boosting. It outlines the topics to be covered in each part, including classical regression, regularized regression techniques like ridge regression, LASSO, and MARS, and ensemble methods like random forests and TreeNet gradient boosted trees. Examples using the Boston housing data set are provided to illustrate some of these techniques.
This document discusses how educational institutions can use data mining software to better understand and support their students. It outlines several areas where data analysis can provide insights, such as predicting student performance based on more than just grades, understanding factors that lead to success or failure and graduation, determining the effectiveness of support programs, identifying which recruitment strategies and financial packages attract students, and predicting those most at risk of dropping out or defaulting on loans. The overall goal is to enhance student outcomes and institutional management through analytics.
Comparison of statistical methods commonly used in predictive modelingSalford Systems
This document compares four statistical methods commonly used in predictive modelling: Logistic Multiple Regression (LMR), Principal Component Regression (PCR), Classification and Regression Tree analysis (CART), and Multivariate Adaptive Regression Splines (MARS). It applies these methods to two ecological data sets to test their accuracy, reliability, ease of use, and implementation in a geographic information system (GIS). The results show that independent data is needed to validate models, and that MARS and CART achieved the best prediction success, although CART models became too complex for cartographic purposes with a large number of data points.
This document discusses Dr. Wayne Danter's research using artificial intelligence tools to predict biological activity of molecular structures. His method involves using CART to analyze public HIV data and build predictive models. CART generates decision trees to identify important variables that predict if a molecule is biologically active against HIV. Dr. Danter then uses MARS and NeuroShell Classifier to further improve prediction accuracy. His proprietary CHEMSASTM algorithm teaches neural networks to relate molecular structure to function for screening potential HIV drugs. Using these methods, Dr. Danter has achieved over 96% accuracy in classifying 311 drugs' activity against HIV.
TreeNet Tree Ensembles & CART Decision Trees: A Winning CombinationSalford Systems
Understand CART decision tree pros/cons, how TreeNet stochastic gradient boosting ca n help overcome single-tree challenges, and what the advantages are when using CART and TreeNet in combination for predictive modeling success.
Salford Systems offers several products for data mining and predictive modeling. The table compares features of their Basic, Pro, ProEx, and Ultra components. The Basic component includes basic modeling, reporting, and automation features. Pro adds additional modeling engines and missing data handling capabilities. ProEx further expands the supported modeling techniques and automations. Ultra provides the most extensive set of features, including additional modeling pipelines, ensemble methods, and tree-based algorithms.
This document provides an introduction to MARS (Multivariate Adaptive Regression Splines), an automated regression modeling tool. MARS can build accurate predictive models for continuous and binary dependent variables by automatically selecting variables, determining transformations and interactions between variables, and handling missing data. It efficiently searches through all possible models to identify an optimal solution. The document explains how MARS works, provides settings to configure MARS, and uses the Boston housing dataset to demonstrate the basic steps of building a MARS model.
The document discusses combining CART (Classification and Regression Tree) and logistic regression models to take advantage of their respective strengths in classification and data mining tasks. It describes how running a logistic regression on the entire dataset using CART terminal node assignments as dummy variables allows the logistic model to find effects across nodes that CART cannot detect. This improves CART's predictions by imposing slopes on cases within nodes and providing a more granular, continuous response than CART alone. The approach also allows compensating for some of CART's weaknesses like coarse-grained responses.
When building a predictive model in SPM, you'll want to know exactly what you did to get your results. This short slide deck will show you how to review your work in the session logs.
The document discusses techniques for compressing and extracting rules from TreeNet models. It describes how TreeNet has achieved high predictive performance but its models can be refined further. Regularized regression can be applied to the trees or nodes in a TreeNet model to combine similar trees, reweight trees, and select a compressed subset of trees without much loss in accuracy. This "model compression" technique aims to simplify TreeNet models for improved deployment while maintaining good predictive performance.
TreeNet is a machine learning technique called stochastic gradient boosting developed by Jerome Friedman. It builds decision tree models in a stage-wise fashion, with each subsequent tree attempting to correct the errors of previous trees, resulting in a very accurate predictive model. TreeNet can handle both classification and regression problems, and has advantages such as being able to capture complex variable interactions and resist overfitting. It provides useful outputs for interpreting models such as variable importance rankings and partial dependency plots.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
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Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
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Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
1. Dan Steinberg
N Scott Cardell
Mykhaylo Golovnya
November, 2011
Salford Systems
2.
3. Data Mining Data Mining Cont.
• Predictive Analytics • Statistics • OLAP
• Machine Learning • Computer science • CART
• Pattern Recognition • Database • SVM
• Artificial Management • NN
Intelligence • Insurance • CRISP-DM
• Business • Finance • CRM
Intelligence • Marketing • KDD
• Data Warehousing • Electrical • Etc.
Engineering
• Robotics
• Biotech and more
4. Data mining is the search for patterns in data
using modern highly automated, computer
intensive methods
◦ Data mining may be best defined as the use of a specific
class of tools (data mining methods) in the analysis of
data
◦ The term search is key to this definition, as is
“automated”
The literature often refers to finding hidden
information in data
5. • Study the phenomenon
• Understand its nature
Science • Try to discover a law
• The laws usually hold for a long time
•Collect some data
•Guess the model (perhaps, using science)
Statistics •Use the data to clarify and/or validate the model
•If looks “fishy”, pick another model and do it
again
• Access to lots of data
• No clue what the model might be
Data Mining • No long term law is even possible
• Let the machine build a model
• And let‟s use this model while we can
6. Quest for the Holy Grail- build an algorithm that will
always find 100% accurate models
Absolute Powers- data mining will finally find and
explain everything
Gold Rush- with the right tool one can rip the stock-
market and become obscenely rich
Magic Wand- getting a complete solution from start
to finish with a single button push
Doomsday Scenario- all conventional analysts will
eventually be replaced by smart computer chips
7. This is known as “supervised learning”
◦ We will focus on patterns that allow us to accomplish two tasks
Classification
Regression
This is known as “unsupervised learning”
◦ We will briefly touch on a third common task
Finding groups in data (clustering, density estimation)
There are other patterns we will not discuss today
including
◦ Patterns in sequences
◦ Connections in networks (the web, social networks, link analysis)
8. CART® (Decision Trees, C4.5, CHAID among others)
MARS® (Multivariate Adaptive Regression Splines)
Artificial Neural Networks (ANNs, many commercial)
Association Rules (Clustering, market basket analysis)
TreeNet® (Stochastic Gradient Tree Boosting)
RandomForests® (Ensembles of trees w/ random splits)
Genetic Algorithms (evolutionary model development)
Self Organizing Maps (SOM, like k-means clustering)
Support Vector Machine (SVM wrapped in many patents)
Nearest Neighbor Classifiers
9. (Insert chart)
In a nutshell: Use historical data to gain
insights and/or make predictions on the new
data
10. Given enough learning iterations, most data mining methods are
capable of explaining everything they see in the input
data, including noise
Thus one cannot rely on conventional (whole sample) statistical
measures of model quality
A common technique is to partition historical data into several
mutually exclusive parts
◦ LEARN set is used to build a sequence of models varying in size and level
of explained details
◦ TEST set is used to evaluate each candidate model and suggest the
optimal one
◦ VALIDATE set is sometimes used to independently confirm the optimal
model performance on yet another sample
11. Historical Data Build a
Sequence of
Learn Models
Monitor
Test
Performance
Confirm
Validate
Findings
12. Analyst needs to indicate where the TEST data is to be
found
◦ Stored in a separate file
◦ Selected at random from the available data
◦ Pre-selected from available data and marked by a special indicator
Other things to consider
◦ Population: LEARN and TEST sets come from different populations
(within-sample versus out-of-sample)
◦ Time: LEARN and TEST sets come from different time periods
(within-time versus out-of-time)
◦ Aggregation: logically grouped records must be all included or all
excluded within each set (self-correlation)
13. Any model is built on past data!
Fortunately, many models trace stable patterns of behavior
However, any model will eventually have to be rebuilt:
◦ Banks like to refresh risk models about every 12 months
◦ Targeted marketing models are typically refreshed every 3 months
◦ Ad web-server models may be refreshed every 24 hours
Credit risk score card expert Professor David
Hand, University of London maintains:
◦ A predictive model is obsolete the day it is first deployed
14.
15. Model evaluation is at the core of the learning process (choosing
the optimal model from a list of candidates)
Model evaluation is also a key part in comparing performance of
different algorithms
Finally, model evaluation is needed to continuously monitor
model performance over time
In predictive modeling (classification and regression) all we need
is a sample of data with known outcome; different evaluation
criteria can then be applied
There will never be “the best for all” model; the optimality is
contingent upon current evaluation criterion and thus depends
on the context in which the model is applied
16. (insert graph)
One usually computes some measure of average
discrepancy between the continuous model predictions f
and the actual outcome y
◦ Least Squared Deviation: R= Σ(y-f)^2
◦ Least Absolute Deviation: R= Σ Iy-fI
Fancier definitions also exist
◦ Huber-M Loss: is defined as a hybrid between the LS and LAD
losses
◦ SVM Loss: ignores very small discrepancies and then switches to
LAD-style
The raw loss value is often re-expressed in relative terms
as R-squared
17. There are three progressively more demanding approaches to
solving binary classification problems
Division: a model makes the final class assignment for each
observation internally
◦ Observations with identical class assignment are no longer discriminated
◦ A model needs to be rebuilt to change decision rules
Rank: a model assigns a continuous score to each observation
◦ The score on its own bears no direct interpretation
◦ But, higher class score means higher likelihood of class presence in
general (without precise quantitative statements)
◦ Any monotone transformation of scores is admissible
◦ A spectrum of decision rules can be constructed strictly based on varying
score threshold without model rebuilding
Probability: a model assigns a probability score to each observation
◦ Same as above, but the output is interpreted directly in the exact probabilistic
terms
18. Depending on the prediction emphasis, various performance evaluation
criteria can be constructed for binary classification models
The following list, far from being exhausting, presents some of the
frequently used evaluation criteria
◦ Accuracy (more generally- Expected Cost)
Applicable to all models
◦ ROC Curve and Area Under Curve
Not Applicable to Division Models
◦ Gains and Lift
Not Applicable to Division Models
◦ Log-likelihood (a.k.a Cross-Entropy, Deviate)
Not Applicable to Division and Rank Models
The criteria above are listed in the order from the least specific to the
most
It is not guaranteed that all criteria will suggest the same model as the
optimal from a list of candidate models
19. Most intuitive and also the weakest evaluation method that can be
applied to any classification model
Each record must be assigned to a specific class
One first constructs a Prediction Success Table- a 2 by 2 matrix showing
how many true 0s and 1s (rows) were classified by the model correctly or
incorrectly (columns)
The classification accuracy is then the number of correct class
assignments divided by the sample size
More general approaches will also include user supplied prior
probabilities and cost matrix to compute the Expected Cost
The example below reports prediction success tables for two separate
models along with the accuracy calculations
The method is not sensitive enough to emphasize larger class unbalance
in model 1
(insert table)
20. The classification accuracy approach assumes that each record
has already been classified which is not always convenient
◦ Those algorithms producing a continuous score (Rank or Probability) will
require a user-specified threshold to make final class assignments
◦ Different thresholds will result to different class assignments and likely
different classification accuracies
The accuracy approach focuses on the separating boundary and
ignores fine probability structure outside the boundary
Ideally, need an evaluator working directly with the score itself
and not dependent on any external considerations like costs and
thresholds
Also, for Rank models the evaluator needs to be invariant with
respect to monotone transformation of the scores so that the
“spirit” of such models is not violated
21. The following approach will take full advantage of the set of
continuous scores produced by Rank or Probability models
Pick one of the two target classes as the class in focus
Sort a database by predicted score in descending order
Choose a set of different score values
◦ Could be ALL of the unique scores produced by the model
◦ More often a set of scores obtained by binning sorted records into equal
size bins
For any fixed value of the score we can now compute:
◦ Sensitivity (a.k.a True Positive): Percent of the class in focus with the
predicted scores above the threshold
◦ Specificity (a.k.a False Positive): Percent of the opposite class with the
predicted scores below the threshold
We then display the results as a plot of [sensitivity] versus [1-specificity]
The resulting curve is known as the ROC Curve
22. (insert graph)
ROC Curves for three different rank models are shown
No model can be considered as the absolute best in all times
The optimal model selection will rest with the user
Average overall performance can be measured as Area Under
ROC Curve (AUC)
◦ ROC Curve (up to orientation) and AUC are invariant with respect to the
focus class selection
◦ The best attainable AUS is always 1.0
◦ AUC of a model with randomly assigned scores is 0.5
AUC can be interpreted
◦ Suppose we randomly and repeatedly pick one observation at random
from the focus class and another observation from the opposite class
◦ Then AUC is the fraction of trials resulting to the focus class observation
having greater predicted score than the opposite class observation
◦ AUC below 0.5 means that something is fundamentally wrong
23. The following example justifies another slightly different approach to model
evaluation
Suppose we want to mail a certain offer to P fraction of the population
Mailing to a randomly chosen sample will capture about P fraction of the
responders (random sampling procedure)
Now suppose that we have access to a response model which ranks each potential
responder by a score
Now if we sample the P fraction of the population targeting members with the
highest predicted scores first (model guided sampling), we could now get T
fraction of the responders which we expect to be higher than P
The lift in P(th) percentile is defined as the ratio T/P
Obviously, meaningful models will always produce lift greater than 1
The process can be repeated for all possible percentiles and the results can be
summarized graphically as Gains and Cumulative Lift curves
In practice, one usually first sorts observations by scores and then partitions
sorted data into a fixed number of bins to save on calculations just like it is
usually done for ROC curves
25. (insert graphs)
Lift in the given percentile provides a point measure of
performance for the given population cutoff
◦ Can be viewed as the relative length of the vertical line segment
connecting the gains curve at the given population cutoff
Area Under the Gains curve (AUG): Provides an integral measure
of performance across all bins
◦ Unlike AUC, the largest attainable value of AUG is (1-p/2), P being the
fraction of responders in the population
Just like ROC-curves, gains and lift curves for different models
can intersect, so that performance-wise one model is better for
one range of cutoffs while another model is better for a different
range
Unlike ROC-curve, gains and lift curves do depend on the class
in focus
◦ For the dominant class, gains and lift curves degenerate to the trivial 45-
degree line random case
26. ROC, Gains, and lift curves together with AUC and AUG are invariant
with respect to monotone transformation of the model scores
◦ Scores are only used to sort records in the evaluation set, the actual score
values are of no consequence
All these measures address the same conceptual phenomenon
emphasizing different sides and thus can be easily derived from
each other
◦ Any point (P,G) on a gains curve corresponds to the point (P,G/P) on the
lift curve
◦ Suppose that the focus class occupies fraction F of the population; then
any point (P,G) on a gains curve corresponds to the point {(P-FG)/(1-F),G}
on the ROC curve
It follows that the ROC graph “pushes” the gains graph “away” from the 45
degree line
Dominant focus class (large F) is “pushed” harder so that the degeneracy of
its gain curve disappears
In contrast, rare focus class (small F) has ROC curve naturally “close” to the
gains curve
All of these measures are widely used as robust performance
evaluations in various practical applications
27. When the output score can be interpreted as probability, a more specific
evaluation criterion can be constructed to access probabilistic accuracy
of the model
We assume that the model generates P(X)-the conditional probability of
1 given X
We also assume that the binary target Y is coded as -1 and +1 (only for
notational convenience)
The Cross-Entropy (CXE) criterion is then computed as (insert equation)
◦ The inner Log computes the log-odds of Y=1
◦ The value itself is the negative log-likelihood assuming independence of
responses
◦ Alternative notation assumes 0/1 target coding and uses the following
formula (insert equation)
◦ The values produced by either of the formula will be identical to each
other
Model with the smallest CXE means the largest likelihood and thus
considered to be the best in terms of capturing the right probability
structure
28. The example shows true non-monotonic conditional probability
(dark blue curve)
We generated 5,000 LEARN and TEST observations based on this
probability model
We report predicted responses generated by different modeling
approaches
◦ Red- best accuracy MART model
◦ Yellow- best CXE MART model
◦ Cyan- univariate LOGIT model
Performance-wise
◦ All models have identical accuracy but the best accuracy model is
substantially worse in terms of CXE
◦ LOGIT can‟t capture departure from monotonicity as reported by CXE
29.
30. MARS is a highly-automated tool for regression
Developed by Jerome H. Friedman of Stanford University
◦ Annals of statistics, 1991 dense 65 page article
◦ Takes some inspiration from its ancestor CART®
◦ Produces smooth curves and surfaces, not the step-functions of CART
Appropriate target variables are continuous
End result of a MARS run is a regression model
◦ MARS automatically chooses which variables to use
◦ Variables are optimally transformed
◦ Interactions are detected
◦ Model is self-tested to protect against over-fitting
Can also perform well on binary dependent variables
◦ Censored survival model (waiting time models as in churn)
31. Harrison, D. and D. Rubinfeld.
Hedonic Housing Prices and Demand for Clean Air. Journal of
Environmental Economics and Management v5, 81-102, 1978
506 census tracts in city of Boston for the year 1970
Goal: study relationship between quality of life variables and property
values
◦ MV- median value of owner-occupied homes in tract („000s)
◦ CRIM- per capita crime rates
◦ NOX- concentration of nitrogen oxides (pphm)
◦ AGE- percent built before 1940
◦ DIS- weighted distance to centers of employment
◦ RM- average number of rooms per house
◦ LSTAT- percent neighborhood „lower socio-economic status‟
◦ RAD- accessibility to radial highways
◦ CHAS- borders Charles River (0/1)
◦ INDUS- percent non-retail business
◦ TAX- tax rate
◦ PT- pupil teacher ratio
32. (insert graph)
The dataset poses significant challenges to
conventional regression modeling
◦ Clearly departure from normality, non-linear
relationships, and skewed distributions
◦ Multicollinearity, mutual dependency, and outlying
observations
33. (insert graph)
A typical MARS solution (univariate for simplicity)
is shown above
◦ Essentially a piece-wise linear regression model with the
continuity requirement at the transition points called
knots
◦ The locations and number of knots were determined
automatically to ensure the best possible model fit
◦ The solution can be analytically expressed as
conventional regression equations
34. Finding the one best knot in a simple regression is a straightforward
search problem
◦ Try a large number of potential knots and choose one with the best R-
squared
◦ Computation can be implemented efficiently using update algorithms;
entire regression does not have to be rerun for every possible knot (just
update X‟X matrices)
Finding k knots simultaneously would require n^k order of
computations assuming N observations
To preserve linear problem complexity, multiple knot
replacement is implemented in a step-wise manner:
◦ Need a forward/backward procedure
◦ The forward procedure adds knots sequentially one at a time
The resulting model will have many knots and overfit the training data
◦ The backward procedure removes least contributing knots one at a time
This produces a list of models of varying complexity
◦ Using appropriate evaluation criterion, identify the optimal model
Resulting model will have approximately correct knot locations
35. (insert graphs)
True conditional mean has two knots at X=30
and X=60, observed data includes additional
random error
Best single knot will be at X=45, subsequent best
locations are true knots around 30 and 60
The backward elimination step is needed to
remove the redundant node at X=45
36. Thinking in terms of knot selection works very well to
illustrate splines in one dimension but unwieldy for
working with a large number of variables simultaneously
◦ Need a concise notation easy to program and extend in multiple
dimensions
◦ Need to support interactions, categorical variables, and missing
values
Basis functions (BF) provide analytical machinery to
express the knot placement strategy
Basis function is a continuous univariate transform that
reduces predictor influence to a smaller range of values
controlled by a parameter c (20 in the example below)
◦ Direct BF: max(X-c, 0)- the original range is cut below c
◦ Mirror BF: max (c-X, 0)- the original range is cut above c
◦ (insert graphs)
37. The following model represents a 3-knot
univariate solution for the Boston Housing
Dataset using two direct and one mirror basis
functions
(insert equations)
All three line segments have negative slope
even though two coefficients are above zero
(insert graph)
38. MARS core technology:
◦ Forward step: add basis function pairs one at a time in conventional step-
wise forward manner until the largest model size (specified by the user) is
reached
Possible collinearity due to redundancy in pairs must be detected and
eliminated
For categorical predictors define basis functions as indicator variables for all
possible subsets of levels
To support interactions, allow cross products between a new candidate pair
and basis functions already present in the model
◦ Backward step: remove basis functions one at a time in conventional step-
wise backward manner to obtain a sequence of candidate models
◦ Use test sample or cross-validation to identify the optimal model size
Missing values are treated by constructing missing value
indicator (MVI) variables and nesting the basis functions within
the corresponding MVIs
Fast update formulae and smart computational shortcuts exist to
make the MARS process as fast and efficient as possible
39. OLS and MARS regression (insert graphs)
We compare the results of classical linear regression
and MARS
◦ Top three significant predictors are shown for each model
◦ Linear regression provides global insights
◦ MARS regression provides local insights and has superior
accuracy
All cut points were automatically discovered by MARS
MARS model can be presented as a linear regression model in
the BF space
40.
41. One of the oldest Data Mining tools for classification
The method was originally developed by Fix and Hodges (1951) in an
unpublished technical report
Later on it was reproduced by Agrawala (1977), Silverman and Jones
(1989)
A review book with many references on the topic is Dasarathy (1991)
Other books that treat the issue:
◦ Ripley B.D. 1996. Pattern Recognition and Neural Networks (chapter 6)
◦ Hastie T, Tibshirani R and Friedman J. 2001. The Elements of Statistical Learning Data
Mining, Inference and Prediction (chapter 13)
The underlying idea is quite simple: make the predictions by proximity
or similarity
Example: we are interested in predicting if a customer will respond to an
offer. A NN classifier will do the following:
◦ Identify a set of people most similar to the customer- the nearest neighbor
◦ Observe what they have done in the past on a similar offer
◦ Classify by majority voting: if most of them are responders, predict a
responder, otherwise, predict a non-responder
42. (insert graphs)
Consider binary classification problem
Want to classify the new case highlighted in yellow
The circle contains the nearest neighbors (the most similar
cases)
◦ Number of neighbors= 16
◦ Votes for blue class= 13
◦ Votes for red class= 3
Classify the new case in the blue class. The estimated probability
of belonging to the blue class is 13/16=0.8125
Similarly in this example:
◦ Classify the yellow instance in the blue class
◦ Classify the green instance in the red class
◦ The black point receives three votes from the blue class and another three
from the red one- the resulting classification is indeterminate
43. There are two decisions that should be made in advance before
applying the NN classifier
◦ The shape of the neighborhood
Answers the question “Who are our nearest neighbors?”
◦ The number of neighbors (neighborhood size)
Answers the question “How many neighbors do we want to consider?”
Neighborhood shape amounts to choosing the
proximity/distance measure
◦ Manhattan distance
◦ Euclidean distance
◦ Infinity distance
◦ Adaptive distances
Neighborhood size K can vary between 1 and N (the dataset size)
◦ K=1-classification is based on the closest case in the dataset
◦ K=N-classification is always to the majority class
◦ Thus K acts as a smoothing parameter and can be determined by using a
test sample or cross-validation
44. NN advantages
◦ Simple to understand and easy to implement
◦ The underlying idea is appealing and makes logical sense
◦ Available for both classification and regression problems
Predictions determined by averaging the values of nearest neighbors
◦ Can produce surprisingly accurate results in a number of
applications
NN have been proved to perform equal or better than LDA, CART, Neural
Networks and other approaches when applied to remote sensed data
NN disadvantages
◦ Unlike decision trees, LDA, or logistic regression, their decision
boundaries are not easy to describe and interpret
◦ No variable selection of any kind- vulnerable to noisy inputs
All the variables have the same weight when computing the distance, so
two cases could be considered similar (or dissimilar) due to the role of
irrelevant features (masking effects)
◦ Subject to the curse of dimensionality in high dimension datasets
◦ The technique is quite time consuming. However, Friedman et. Al.
(1975 and 1977) have proposed fast algorithms
45.
46. Classification and Regression Trees (CART®)- original approach
based on the “let the data decide local regions” concept
developed by Breiman, Friedman, Olshen, and Stone in 1984
The algorithm can be summarized as:
◦ For each current data region, consider all possible orthogonal splits (based
on one variable) into 2 sub-regions
◦ The best split is defined as the one having the smallest MSE after fitting a
constant in each sub-region (regression) or the smallest resulting class
impurity (classification)
◦ Proceed recursively until all structure in the training set has been
completely exhausted- largest tree is produced
◦ Create a sequence of nested sub-trees with different amount of
localization (tree pruning)
◦ Pick the best tree based on the performance on a test set or cross-
validated
One can view CART tree as a set of dynamically constructed
orthogonal nearest neighbor boxes of varying sizes guided by
the response variable (homogeneity of response within each box)
47. CART is best illustrated with a famous example- the UCSD Heart
Disease study
◦ Given the diagnosis of a heart attack based on
Chest pain, Indicative EKGs, Elevation of enzymes typically released by
damaged heart muscle, etc.
◦ Predict who is at risk of a 2nd heart attack and early death within 30 days
◦ Prediction will determine treatment program (intensive care or not)
For each patient about 100 variables were available, including:
◦ Demographics, medical history, lab results
◦ 19 noninvasive variables were used in the analysis
Age, gender, blood pressure, heart rate, etc.
CART discovered a very useful model utilizing only 3 final
variables
48. (insert classification tree)
Example of a CLASSIFICATION tree
Dependent variable is categorical (SURVIVE, DIE)
The model structure is inherently hierarchical and cannot be represented
by an equivalent logistic regression equation
Each terminal node describes a segment in the population
All internal splits are binary
Rules can be extracted to describe each terminal node
Terminal node class assignment is determined by the distribution of the
target in the node itself
The tree effectively compresses the decision logic
49. CART advantages:
◦ One of the fastest data mining algorithms available
◦ Requires minimal supervision and produces easy to understand
models
◦ Focuses on finding interactions and signal discontinuities
◦ Important variables are automatically identified
◦ Handles missing values via surrogate splits
A surrogate split is an alternative decision rule supporting the main rule
by exploiting local rank-correlation in a node
◦ Invariant to monotone transformations of predictors
CART disadvantages:
◦ Model structure is fundamentally different from conventional
modeling paradigms- may confuse reviewers and classical
modelers
◦ Has limited number of positions to accommodate available
predictors- ineffective at presenting global linear structure (but
great for interactions)
◦ Produces coarse-grained piece-wise constant response surfaces
50. (insert charts)
10-node CART tree was built on the cell phone dataset
introduced earlier
The root Node 1 displays details of TARGET variable in the
training data
◦ 15.2% of the 830 households accepted the marketing offer
CART tried all variable predictors one at a time and found out
that partitioning the set of subjects based on the Handset Price
variable is most effective at separating responders from non-
responders at this point
◦ Those offered the phone with a price>130 contain only 9.9% responders
◦ Those offered a lower price<130 respond at 21.9%
The process of splitting continues recursively until the largest
tree is grown
Subsequent tree pruning eliminates least important branches
and creates a sequence of nested trees- candidate models
51. (insert charts)
The red nodes indicate good responders while the blue nodes
indicate poor responders
Observations with high values on a split variable always go right
while those with low values go left
Terminal nodes are numbered left to right and provide the
following useful insights
◦ Node 1: young prospects having very small phone bill, living in specific
cities are likely to respond to an offer with a cheap handset
◦ Node 5: mature prospects having small phone bill, living in specific cities
(opposite Node1) are likely to respond to an offer with a cheap handset
◦ Nodes 6 and 8: prospects with large phone bill are likely to respond as
long as the handset is cheap
◦ Node 10: “high-tech” prospects (having a pager) with large phone bill are
likely to respond to even offers with expensive handset
52. (insert graph, table and chart)
A number of variables were identified as
important
◦ Note the presence of surrogates not seen on the main
tree diagram previously
Prediction Success table reports classification
accuracy on the test sample
Top decile (10% of the population with the
highest scores) captures 40% of the responders
(lift of 4)
53. (insert graphs)
CART has a powerful mechanism of priors built
into the core of the tree building mechanism
Here we report the results of an experiment with
prior on responders varying from 0.05 to 0.95 in
increments of 0.05
The resulting CART models “sweep” the modeling
space enforcing different sensitivity-specificity
tradeoff
54. As prior on the given class decreases
The class assignment threshold increases
Node richness goes up
But class accuracy goes down
PRIORS EQUAL uses the root node class ratio as the class assignment
threshold- hence, most favorable conditions to build a tree
PRIORS DATA uses the majority rule as the class assignment threshold-
hence, difficult modeling conditions on unbalanced classes.
In reality, a proper combination of priors can be found experimentally
Eventually, when priors are too extreme, CART will refuse to build a tree.
◦ Often the hottest spot is a single node in the tree built with the most
extreme priors with which CART will still build a tree.
◦ Comparing hotspots in successive trees can be informative, particularly in
moderately-sized data sets.
55. (insert graph)
We have a mixture of two overlapping classes
The vertical lines show root node splits for
different sets of priors. (the left child is classified
as red, the right child is classified as blue)
Varying priors provides effective control over the
tradeoff between class purity and class accuracy
56. Hot spots are areas of data very rich in the event of interest, even
though they could only cover a small fraction of the targeted
group
◦ A set of prospects rich in responders
◦ A set of transactions with abnormal amount of fraud
The varying-priors collection of runs introduced above gives
perfect raw material in the search of hot spots
◦ Simply look at all terminal nodes across all trees in the collection and
identify the highest response segments
◦ Also want to have such segments as large as possible
◦ Once identified, the rules leading to such segments (nodes) are easily
available
◦ (insert graph)
◦ The graph on the left reports all nodes according to their target coverage
and lift
◦ The blue curve connects the nodes most likely to be a hot spot
57. (insert graph)
Our next experiment (variable shaving) runs as follows:
◦ Build a CART model with the full set of predictors
◦ Check the variable importance, remove the least important
variable and rebuild CART model
◦ Repeat previous step until all variables have been removed
Six-variable model has the best performance so far
Alternative shaving techniques include:
◦ Proceed by removing the most important variable- useful in
removal of model “hijackers”- variables looking very strong on the
train data but failing on the test data (e.g. ID variables)
◦ Set up nested looping to remove redundant variables from the
inner positions on the variable importance list
58. (insert tree)
Many predictive models benefit from Salford Systems
patent on “Structured Trees”
Trees constrained in how they are grown to reflect
decision support requirements
◦ Variables allowed/disallowed depending on a level in a tree
◦ Variable allowed/disallowed depending on a node size
In mobile phone example: want tree to first segment on
customer characteristics and then complete using price
variables
◦ Price variables are under the control of the company
◦ Customer characteristics are beyond company control
59. Various areas of research were spawned by CART
We report on some of the most interesting and well developed
approaches
Hybrid models
◦ Combining CART with linear and Logistic Regression
◦ Combining CART with Neural Nets
Linear combination splits
Committees of trees
◦ Bagging
◦ Arcing
◦ Random Forest
Stochastic Gradient Boosting (MART a.k.a TreeNet)
Rule Fit and Path Finder
60.
61. (insert images)
Grow a tree on training data
Find a way to grow another tree, different from currently
available (change something in set up)
Repeat many times, say 500 replications
Average results or create voting scheme
◦ For example, relate PD to fraction of trees predicting default for a given
Beauty of the method is that every new tree starts with a
complete set of data
Any one tree can run out of data, but when that happens we just
start again with a new tree and all the data (before sampling)
62. Have a training set of size N
Create a new data set of size N by doing sampling with
replacement from the training set
The new set (called bootstrap sample) will be different from the
original:
◦ 36.5% of the original records are excluded
◦ 37.5% of the original records are included once
◦ 18% of the original records are included twice
◦ 6% of the original records are included three times
◦ 2% of the original records are included four or more times
May do this repeatedly to generate numerous bootstrap samples
Example: distribution of record weights in one realized bootstrap
sample
(insert table)
63. To generate predicted response, multiple trees are combined via
voting (classification) or averaging (regression) schemas
Classification trees “vote”
◦ Recall that classification trees classify
Assign each case to ONE class only
◦ With 100 trees, 100 separate class assignment (votes) for each record
◦ Winner is the class with the most votes
◦ Fraction of votes can be used as a crude approximation to class
probability
◦ Votes could be weighted- say by accuracy of individual trees or node sizes
◦ Class weights can be introduced to counter the effects of dominant classes
Regression trees assign a real predicted value for each case
◦ Predictions are combined via averaging
◦ Results will be much smoother than from a single tree
64. Breiman reports the results of running bootstrap
aggregation (bagger) on four publicly available
datasets from Statlog project
In all cases the bagger shows substantial
improvement in the classification accuracy
It all comes at a price of no longer having a
single interpretable model, substantially longer
run time and greater demand on model storage
space
(insert tables)
65. Bagging proceeds by independent, identically-distributed
sampling draws
Adaptive resampling: probability that a case is sampled varies
dynamically
◦ Cases with higher current prediction errors have greater probability of
being sampled in the next round
◦ Idea is to focus on these cases most difficult to predict correctly
Similar procedure first introduced by Freund & Schapire (1996)
Breiman variant (ARC-x4) is easier to understand:
◦ Suppose we have already grown K trees: let m= # times case i was
misclassified (0≤m≤k) (insert equations)
◦ Weight=1 for cases with zero occurrences of misclassification
◦ Weight= 1+k^4 for cases with K misclassifications
Weigh rapidly becomes large is case is difficult to classify
66. The results of running bagger and ARCer on the Boston
Housing Data are reported below
Bagger shows substantial improvement over the single-
tree model
ARCer shows marginal improvement over the bagger
(insert table)
Single tree now performs worse than stand alone CART run
(R-squared=72%) because in bagging we always work with
exploratory trees only
Arcing performance beats MARS additive model but is still
inferior to the MARS interactions model
67. Boosting (and Bagging) are very slow and consume a lot of
memory, the final models tend to be awkwardly large and
unwieldy
Boosting in general is vulnerable to overtraining
◦ Much better fit on training than on test data
◦ Tendency to perform poorly on future data
◦ Important to employ additional considerations to reduce overfitting
Boosting is also highly vulnerable to errors in the data
◦ Technique designed to obsess over errors
◦ Will keep trying to “learn” patterns to predict miscoded data
◦ Ideally would like to be able to identify miscoded and outlying data and
exclude those records from the learning process
◦ Documented in study by Dietterich (1998)
An Experimental Comparison of Three Methods for Constructing Ensembles
of Decision Trees, Bagging, Boosting, and Randomization
68.
69. New approach for many data analytical tasks developed by
Leo Breiman of University of California, Berkeley
◦ Co-author of CART® with Friedman, Olshen, and Stone
◦ Author of Bagging and Arcing approaches to combining trees
Good for classification and regression problems
◦ Also for clustering, density estimation
◦ Outlier and anomaly detection
◦ Explicit missing value imputation
Builds on the notions of committees of experts but is
substantially different in key implementation details
70. A random forest is a collection of single trees grown in a
special way
◦ Each tree is grown on a bootstrap sample from the learning set
◦ A number R is specified (square root by defualt) such that is
noticeably smaller than the total number of available predictors
◦ During tree growing phase, at each node only R predictors are
randomly selected and tried
The overall prediction is determined by voting (in
classification) or averaging (in regression)
The law of Large Numbers ensures convergence
The key to accuracy is low correlation and bias
To keep bias low, trees are grown to maximum depth
71. Randomness is introduced in two distinct ways
Each tree is grown on a bootstrap sample from the learning set
◦ Default bootstrap sample size equals original sample size
◦ Smaller bootstrap sample sizes are sometimes useful
A number R is specified (square root by default) such that it is
noticeably smaller than the total number of available predictors
During tree growing phase, at each node only R predictors are
randomly selected and tried.
Randomness also reduces the signal to noise ratio in a single
tree
◦ A low correlation between trees is more important than a high signal when
many trees contribute to forming the model
◦ RandomForests™ trees often have very low signal strength, even when the
signal strength of the forest is high.
72. (insert graph)
Gold- Average of 50 Base Learners
Blue- Average of 100 Base Learners
Red- Average of 500 Base Learners
73. (insert graph)
Averaging many base learners improves the
signal to noise ratio dramatically provided
that the correlation of errors is kept low
Hundreds of base learners are needed for the
most noticeable effect
74. All major advantages of a single tree are automatically preserved
Since each tree is grown on a bootstrap sample, one can
◦ Use out of bag samples to compute an unbiased estimate of the accuracy
◦ Use out of bag samples to determine variable importances
There is no overfitting as the number of trees increases
It is possible to compute generalized proximity between any pair
of cases
Based on proximities one can
◦ Proceed with a target-driven clustering solution
◦ Detect outliers
◦ Generate informative data views/projections using scaling coordinates
◦ Do missing value imputation
Interesting approaches to expanding the methodology into
survival models and the unsupervised learning domain
75. RF introduces a novel way to define proximity between two observations:
◦ For a dataset of size N define an NXN matrix of proximities
◦ Initialize all proximities to zeroes
◦ For any given tree, apply the tree to the dataset
◦ If case i and case j both end up in the same node, increase proximity
proxij between i and j by one
◦ Accumulate over all trees in RF and normalize by twice the number of trees
in RF
The resulting matrix provides intrinsic measure of proximity
◦ Observations that are “alike” will have proximities close to one
◦ The closer the proximity to 0, the more dissimilar cases i and j are
◦ The measure is invariant to monotone transformations
◦ The measure is clearly defined for any type of independent
variables, including categorical
76.
77. TreeNet (TN) is a new approach to machine learning and function
approximation developed by Jerome H, Friedman at Stanford
University
◦ Co-author of CART® with Breiman, Olshen and Stone
◦ Author of MARS®, PRIM, Projection Pursuit, COSA, RuleFit™ and more
Also known as Stochastic Gradient Boosting and MART (Multiple
Additive Regression Trees)
Naturally supports the following classes of predictive models
◦ Regression (continuous target, LS and LAD loss functions)
◦ Binary Classification (binary target, logistic likelihood loss function)
◦ Multinomial classification (multiclass target, multinomial likelihood loss
function)
◦ Poisson regression (counting target, Poisson Likelihood loss function)
◦ Exponential survival (positive target with censoring)
◦ Proportional hazard cox survival model
TN builds on the notions of committees of experts and boosting
but is substantially different in key implementation details
78. We focus on TreeNet because:
It is the method introduced in the original Stochastic Gradient
Boosting article
It is the method used in many successful real world studies
We have found it to be more accurate than the other methods
◦ Many decisions that affect many people are made using a TreeNet model
◦ Major new fraud detection engine uses TreeNet
◦ David Cossock of Yahoo recently published a paper on uses of TreeNet in
web search
TreeNet is a fully developed methodology. New capabilities
include:
◦ Graphical display of the impact of any predictor
◦ New automated ways to test for existence of interactions
◦ New ways to identify and rank interactions
◦ Ability to constrain model: allow some interactions and disallow others.
◦ Method to recast TreeNet model as a logistic regression.
79. Built on CART trees and thus
◦ Immune to outliers
◦ Selects variables
◦ Results invariant with monotone transformations of variables
◦ Handles missing values automatically
Resistant to mislabeled target data
◦ In medicine cases are commonly misdiagnosed
◦ In business, occasionally non-responders flagged as “responders”
Resistant to overtraining- generalizes very well
Can be remarkably accurate with little effort
Trains very rapidly; comparable to CART
80. 2007 PAKDD competition: home loans up-sell to credit card owners
2nd place
◦ Model built in half a day using previous year submission as a blueprint
2006 PAKDD competition: customer type discrimination 3rd place
◦ Model built in one day. 1st place accuracy 81.9% TreeNet accuracy 81.2%
2005 BI-CUP Sponsored by University of Chile attracted 60 competitors
2004 KDDCup “Most Accurate”
2003 “Duke University/NCR Teradata CRN modeling competition
◦ Most Accurate and Best Top Decile Lift on both in and out of time samples
A major financial services company has tested TreeNet across a
broad range of targeted marketing and risk models for the past two
years
◦ TreeNet consistently outperforms previous best models (around 10%
AUROC)
◦ TreeNet models can be built in a fraction of the time previously devoted
◦ TreeNet reveals previously undetected predictive power in data
81. Begin with one very small tree as initial model
◦ Could be as small as ONE split generating 2 terminal nodes
◦ Typical model will have 3-5 splits in a tree, generating 4-6 terminal nodes
◦ Output is a continuous response surface regardless of the target type
Hence, Probability modeling type for classification
◦ Model is intentionally “weak”- shrink all model predictions towards zero
by multiplying all predictions by a small positive learn rate
Compute “residuals” for this simple model (prediction error) for
every record in data
◦ The actual definition of the residual in this case is driven by the type of the
loss function
Grow second small tree to predict the residuals from first tree
Continue adding more and more trees until a reasonable amount
has been added
◦ It is important to monitor accuracy on an independent test sample
83. Trees are kept small (2-6 nodes common)
Updates are small- can be as small as .01,.001,.0001
Use random subsets of the training data in each cycle
◦ Never train on all the training data in any one cycle
Highly problematic cases are IGNORED
◦ If model prediction starts to diverge substantially from observed data, that
data will not be used in further updates
TN allows very flexible control over interactions:
◦ Strictly Additive Models (no interactions allowed)
◦ Low level interactions allowed
◦ High level interactions allowed
◦ Constraints: only specific interactions allowed (TN PRO)
84. As TN models consist of hundreds or even thousands of trees there is no
useful way to represent the model via a display of one or two trees
However, the model can be summarized in a variety of ways
◦ Partial Dependency Plots: These exhibit the relationship between the
target and any predictor- as captured by the model
◦ Variable Importance Rankings: These stable rankings give an excellent
assessment of the relative importance of predictors
◦ ROC and Gains Curves: TN Models produce scores that are typically unique
for each scored record
◦ Confusion Matrix: Using an adjustable score threshold this matrix displays
the model false positive and false negative rates
TreeNet models based on 2-node trees by definition EXCLUDE interactions
◦ Model may be highly nonlinear but is by definition strictly additive
◦ Every term in the model is based on a single variable (single split)
Build TreeNet on a larger tree (default is 6 nodes)
◦ Permits up to 5-way interaction but in practice is more like 3-way interaction
Can conduct informal likelihood ratio test TN(2-node) versus TN(6-
node)
Large differences signal important interactions
85. (insert graphs)
The results of running TN on the Boston
Housing Database are shown
All of the key insights agree with previous
findings by MARS and CART
86. Slope reverses due to interaction
Note that the dominant pattern is downward
sloping, but that a key segment defined by
the 3rd variable is upward sloping
(insert graph)
87.
88. CART: Model is one optimized Tree
◦ Model is easy to interpret as rules
Can be useful for data exploration, prior to attempting a more complex
model
◦ Model can be applied quickly with a variety of workers:
A series of questions for phone bank operators to detect fraudulent
purchases
Rapid triage in hospital emergency rooms
◦ In some cases may produce the best or the most predictive model, for example
in classification with a barely detectable signal
◦ Missing values handled easily and naturally. Can be deployed effectively even
when new data have a different missingness pattern
Random Forests: combination of many LARGE trees
◦ Unique nonparametric distance metric that works in high dimensional spaces
◦ Often predicts well when other models work poorly, e.g. data with high level
interactions
◦ In the most difficult data sets can be the best way to identify important
variables
Tree Net: combination of MANY small trees
◦ Best overall forecast performance in many cases
◦ Constrained models can be used to test the complexity of the data structure
non-parametrically
◦ Exceptionally good with binary targets
89. Neural Networks, combination of a few sigmoidal activation
functions
◦ Very complex models can be represented in a very compact form
◦ Can accurately forecast both levels and slopes and even higher order
derivatives
◦ Can efficiently use vector dependent variables
Cross equation constraints can be imposed. (see Symmetry constraints for
feedforward network models of gradient systems, Cardell, Joerding, and
Li, IEEE Transactions on Neural Networks, 1993)
◦ During deployment phase, forecasts can be computed very quickly
High voltage transmission lines use a neural network to detect whether there
has been a lightning strike and are fast enough to shut down the line before
it can be damaged
Kernel function estimators, use a local mean or a local regression
◦ Local estimates easy to understand and interpret
◦ Local regression versions can estimate slopes and levels
◦ Initial estimation can be quick
90. Random Forests:
◦ Models are large, complex and un-interpretable
◦ Limited to moderate sample sizes (usually less than 100,000
observations)
◦ Hard to tell in advance which case Random Forests will work well
on
◦ Deployed models require substantial computation
Tree Net
◦ Models are large and complex, interpretation requires additional
work
◦ Deployed models either require substantial computation or post-
processing of the original model into a more compact form
CART
◦ In most cases models are less accurate than TreeNet
◦ Works poorly in cases where effects are approximately linear in
continuous variables or additive over many variables
91. Neural Networks:
◦ Neural Networks cover such a wide variety of models that no good widely-
applicable modeling software exists or may even be possible
The most dramatic successes have been with Neural Network models that are
idiosyncratic to the specific case, and ere developed with great effort
Fully optimized Neural Network parameter estimates can be very difficult to
compute, and sometimes perform substantially worse than initial statistically
inferior estimates. (this is called the “over training” issue)
◦ In almost all cases initial estimation is very compute intensive
◦ Limited to very small numbers of variables (typically between about 6 and
20 depending on the application)
Kernel Function Estimators:
◦ Deployed models can require substantial computation
◦ Limited to small numbers of variables
◦ Sensitive to distance measures. Even a modest number of variables can
degrade performance substantially, due to the influence of relatively
unimportant variables on the distance metric
92. Breiman, L., J. Friedman, R. Olshen and C. Stone
(1984), Classification and Regression Trees, Pacific Grove:
Wadsworth
Breiman, L. (1996). Bagging predictors. Machine
Learning, 24, 123-140.
Hastie, T., Tibshirani, R., and Friedman, J.H (2000). The
Elements of Statistical Learning. Springer.
Freund, Y. & Schapire, R. E. (1996). Experiments with a
new boosting algorithm. In L. Saitta, ed., Machine
Learning: Proceedings of the Thirteenth National
Conference, Morgan Kaufmann, pp. 148-156.
Friedman, J.H. (1999). Stochastic gradient boosting.
Stanford: Statistics Department, Stanford University.
Friedman, J.H. (1999). Greedy function approximation: a
gradient boosting machine. Stanford: Statistics
Department, Stanford University.